org.apache.iceberg.flink.sink.shuffle.MapRangePartitioner Maven / Gradle / Ivy
Show all versions of iceberg-flink-1.18 Show documentation
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package org.apache.iceberg.flink.sink.shuffle;
import java.util.Arrays;
import java.util.Comparator;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.NavigableMap;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
import org.apache.flink.api.common.functions.Partitioner;
import org.apache.flink.table.data.RowData;
import org.apache.iceberg.Schema;
import org.apache.iceberg.SortKey;
import org.apache.iceberg.SortOrder;
import org.apache.iceberg.SortOrderComparators;
import org.apache.iceberg.StructLike;
import org.apache.iceberg.flink.FlinkSchemaUtil;
import org.apache.iceberg.flink.RowDataWrapper;
import org.apache.iceberg.relocated.com.google.common.annotations.VisibleForTesting;
import org.apache.iceberg.relocated.com.google.common.base.MoreObjects;
import org.apache.iceberg.relocated.com.google.common.base.Preconditions;
import org.apache.iceberg.relocated.com.google.common.collect.Lists;
import org.apache.iceberg.relocated.com.google.common.collect.Maps;
import org.apache.iceberg.util.Pair;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* Internal partitioner implementation that supports MapDataStatistics, which is typically used for
* low-cardinality use cases. While MapDataStatistics can keep accurate counters, it can't be used
* for high-cardinality use cases. Otherwise, the memory footprint is too high.
*
* It is a greedy algorithm for bin packing. With close file cost, the calculation isn't always
* precise when calculating close cost for every file, target weight per subtask, padding residual
* weight, assigned weight without close cost.
*
*
All actions should be executed in a single Flink mailbox thread. So there is no need to make
* it thread safe.
*/
class MapRangePartitioner implements Partitioner {
private static final Logger LOG = LoggerFactory.getLogger(MapRangePartitioner.class);
private final RowDataWrapper rowDataWrapper;
private final SortKey sortKey;
private final Comparator comparator;
private final Map mapStatistics;
private final double closeFileCostInWeightPercentage;
// Counter that tracks how many times a new key encountered
// where there is no traffic statistics learned about it.
private long newSortKeyCounter;
private long lastNewSortKeyLogTimeMilli;
// lazily computed due to the need of numPartitions
private Map assignment;
private NavigableMap sortedStatsWithCloseFileCost;
MapRangePartitioner(
Schema schema,
SortOrder sortOrder,
MapDataStatistics dataStatistics,
double closeFileCostInWeightPercentage) {
dataStatistics
.statistics()
.entrySet()
.forEach(
entry ->
Preconditions.checkArgument(
entry.getValue() > 0,
"Invalid statistics: weight is 0 for key %s",
entry.getKey()));
this.rowDataWrapper = new RowDataWrapper(FlinkSchemaUtil.convert(schema), schema.asStruct());
this.sortKey = new SortKey(schema, sortOrder);
this.comparator = SortOrderComparators.forSchema(schema, sortOrder);
this.mapStatistics = dataStatistics.statistics();
this.closeFileCostInWeightPercentage = closeFileCostInWeightPercentage;
this.newSortKeyCounter = 0;
this.lastNewSortKeyLogTimeMilli = System.currentTimeMillis();
}
@Override
public int partition(RowData row, int numPartitions) {
// assignment table can only be built lazily when first referenced here,
// because number of partitions (downstream subtasks) is needed.
// the numPartitions is not available in the constructor.
Map assignmentMap = assignment(numPartitions);
// reuse the sortKey and rowDataWrapper
sortKey.wrap(rowDataWrapper.wrap(row));
KeyAssignment keyAssignment = assignmentMap.get(sortKey);
if (keyAssignment == null) {
LOG.trace(
"Encountered new sort key: {}. Fall back to round robin as statistics not learned yet.",
sortKey);
// Ideally unknownKeyCounter should be published as a counter metric.
// It seems difficult to pass in MetricGroup into the partitioner.
// Just log an INFO message every minute.
newSortKeyCounter += 1;
long now = System.currentTimeMillis();
if (now - lastNewSortKeyLogTimeMilli > TimeUnit.MINUTES.toMillis(1)) {
LOG.info("Encounter new sort keys in total {} times", newSortKeyCounter);
lastNewSortKeyLogTimeMilli = now;
}
return (int) (newSortKeyCounter % numPartitions);
}
return keyAssignment.select();
}
@VisibleForTesting
Map assignment(int numPartitions) {
if (assignment == null) {
long totalWeight = mapStatistics.values().stream().mapToLong(l -> l).sum();
double targetWeightPerSubtask = ((double) totalWeight) / numPartitions;
long closeFileCostInWeight =
(long) Math.ceil(targetWeightPerSubtask * closeFileCostInWeightPercentage / 100);
this.sortedStatsWithCloseFileCost = Maps.newTreeMap(comparator);
mapStatistics.forEach(
(k, v) -> {
int estimatedSplits = (int) Math.ceil(v / targetWeightPerSubtask);
long estimatedCloseFileCost = closeFileCostInWeight * estimatedSplits;
sortedStatsWithCloseFileCost.put(k, v + estimatedCloseFileCost);
});
long totalWeightWithCloseFileCost =
sortedStatsWithCloseFileCost.values().stream().mapToLong(l -> l).sum();
long targetWeightPerSubtaskWithCloseFileCost =
(long) Math.ceil(((double) totalWeightWithCloseFileCost) / numPartitions);
this.assignment =
buildAssignment(
numPartitions,
sortedStatsWithCloseFileCost,
targetWeightPerSubtaskWithCloseFileCost,
closeFileCostInWeight);
}
return assignment;
}
@VisibleForTesting
Map mapStatistics() {
return mapStatistics;
}
/**
* Returns assignment summary for every subtask.
*
* @return assignment summary for every subtask. Key is subtaskId. Value pair is (weight assigned
* to the subtask, number of keys assigned to the subtask)
*/
Map> assignmentInfo() {
Map> assignmentInfo = Maps.newTreeMap();
assignment.forEach(
(key, keyAssignment) -> {
for (int i = 0; i < keyAssignment.assignedSubtasks.length; ++i) {
int subtaskId = keyAssignment.assignedSubtasks[i];
long subtaskWeight = keyAssignment.subtaskWeightsExcludingCloseCost[i];
Pair oldValue = assignmentInfo.getOrDefault(subtaskId, Pair.of(0L, 0));
assignmentInfo.put(
subtaskId, Pair.of(oldValue.first() + subtaskWeight, oldValue.second() + 1));
}
});
return assignmentInfo;
}
private Map buildAssignment(
int numPartitions,
NavigableMap sortedStatistics,
long targetWeightPerSubtask,
long closeFileCostInWeight) {
Map assignmentMap =
Maps.newHashMapWithExpectedSize(sortedStatistics.size());
Iterator mapKeyIterator = sortedStatistics.keySet().iterator();
int subtaskId = 0;
SortKey currentKey = null;
long keyRemainingWeight = 0L;
long subtaskRemainingWeight = targetWeightPerSubtask;
List assignedSubtasks = Lists.newArrayList();
List subtaskWeights = Lists.newArrayList();
while (mapKeyIterator.hasNext() || currentKey != null) {
// This should never happen because target weight is calculated using ceil function.
if (subtaskId >= numPartitions) {
LOG.error(
"Internal algorithm error: exhausted subtasks with unassigned keys left. number of partitions: {}, "
+ "target weight per subtask: {}, close file cost in weight: {}, data statistics: {}",
numPartitions,
targetWeightPerSubtask,
closeFileCostInWeight,
sortedStatistics);
throw new IllegalStateException(
"Internal algorithm error: exhausted subtasks with unassigned keys left");
}
if (currentKey == null) {
currentKey = mapKeyIterator.next();
keyRemainingWeight = sortedStatistics.get(currentKey);
}
assignedSubtasks.add(subtaskId);
if (keyRemainingWeight < subtaskRemainingWeight) {
// assign the remaining weight of the key to the current subtask
subtaskWeights.add(keyRemainingWeight);
subtaskRemainingWeight -= keyRemainingWeight;
keyRemainingWeight = 0L;
} else {
// filled up the current subtask
long assignedWeight = subtaskRemainingWeight;
keyRemainingWeight -= subtaskRemainingWeight;
// If assigned weight is less than close file cost, pad it up with close file cost.
// This might cause the subtask assigned weight over the target weight.
// But it should be no more than one close file cost. Small skew is acceptable.
if (assignedWeight <= closeFileCostInWeight) {
long paddingWeight = Math.min(keyRemainingWeight, closeFileCostInWeight);
keyRemainingWeight -= paddingWeight;
assignedWeight += paddingWeight;
}
subtaskWeights.add(assignedWeight);
// move on to the next subtask
subtaskId += 1;
subtaskRemainingWeight = targetWeightPerSubtask;
}
Preconditions.checkState(
assignedSubtasks.size() == subtaskWeights.size(),
"List size mismatch: assigned subtasks = %s, subtask weights = %s",
assignedSubtasks,
subtaskWeights);
// If the remaining key weight is smaller than the close file cost, simply skip the residual
// as it doesn't make sense to assign a weight smaller than close file cost to a new subtask.
// this might lead to some inaccuracy in weight calculation. E.g., assuming the key weight is
// 2 and close file cost is 2. key weight with close cost is 4. Let's assume the previous
// task has a weight of 3 available. So weight of 3 for this key is assigned to the task and
// the residual weight of 1 is dropped. Then the routing weight for this key is 1 (minus the
// close file cost), which is inaccurate as the true key weight should be 2.
// Again, this greedy algorithm is not intended to be perfect. Some small inaccuracy is
// expected and acceptable. Traffic distribution should still be balanced.
if (keyRemainingWeight > 0 && keyRemainingWeight <= closeFileCostInWeight) {
keyRemainingWeight = 0;
}
if (keyRemainingWeight == 0) {
// finishing up the assignment for the current key
KeyAssignment keyAssignment =
new KeyAssignment(assignedSubtasks, subtaskWeights, closeFileCostInWeight);
assignmentMap.put(currentKey, keyAssignment);
assignedSubtasks.clear();
subtaskWeights.clear();
currentKey = null;
}
}
return assignmentMap;
}
/** Subtask assignment for a key */
@VisibleForTesting
static class KeyAssignment {
private final int[] assignedSubtasks;
private final long[] subtaskWeightsExcludingCloseCost;
private final long keyWeight;
private final long[] cumulativeWeights;
/**
* @param assignedSubtasks assigned subtasks for this key. It could be a single subtask. It
* could also be multiple subtasks if the key has heavy weight that should be handled by
* multiple subtasks.
* @param subtaskWeightsWithCloseFileCost assigned weight for each subtask. E.g., if the
* keyWeight is 27 and the key is assigned to 3 subtasks, subtaskWeights could contain
* values as [10, 10, 7] for target weight of 10 per subtask.
*/
KeyAssignment(
List assignedSubtasks,
List subtaskWeightsWithCloseFileCost,
long closeFileCostInWeight) {
Preconditions.checkArgument(
assignedSubtasks != null && !assignedSubtasks.isEmpty(),
"Invalid assigned subtasks: null or empty");
Preconditions.checkArgument(
subtaskWeightsWithCloseFileCost != null && !subtaskWeightsWithCloseFileCost.isEmpty(),
"Invalid assigned subtasks weights: null or empty");
Preconditions.checkArgument(
assignedSubtasks.size() == subtaskWeightsWithCloseFileCost.size(),
"Invalid assignment: size mismatch (tasks length = %s, weights length = %s)",
assignedSubtasks.size(),
subtaskWeightsWithCloseFileCost.size());
subtaskWeightsWithCloseFileCost.forEach(
weight ->
Preconditions.checkArgument(
weight > closeFileCostInWeight,
"Invalid weight: should be larger than close file cost: weight = %s, close file cost = %s",
weight,
closeFileCostInWeight));
this.assignedSubtasks = assignedSubtasks.stream().mapToInt(i -> i).toArray();
// Exclude the close file cost for key routing
this.subtaskWeightsExcludingCloseCost =
subtaskWeightsWithCloseFileCost.stream()
.mapToLong(weightWithCloseFileCost -> weightWithCloseFileCost - closeFileCostInWeight)
.toArray();
this.keyWeight = Arrays.stream(subtaskWeightsExcludingCloseCost).sum();
this.cumulativeWeights = new long[subtaskWeightsExcludingCloseCost.length];
long cumulativeWeight = 0;
for (int i = 0; i < subtaskWeightsExcludingCloseCost.length; ++i) {
cumulativeWeight += subtaskWeightsExcludingCloseCost[i];
cumulativeWeights[i] = cumulativeWeight;
}
}
/**
* Select a subtask for the key.
*
* @return subtask id
*/
int select() {
if (assignedSubtasks.length == 1) {
// only choice. no need to run random number generator.
return assignedSubtasks[0];
} else {
long randomNumber = ThreadLocalRandom.current().nextLong(keyWeight);
int index = Arrays.binarySearch(cumulativeWeights, randomNumber);
// choose the subtask where randomNumber < cumulativeWeights[pos].
// this works regardless whether index is negative or not.
int position = Math.abs(index + 1);
Preconditions.checkState(
position < assignedSubtasks.length,
"Invalid selected position: out of range. key weight = %s, random number = %s, cumulative weights array = %s",
keyWeight,
randomNumber,
cumulativeWeights);
return assignedSubtasks[position];
}
}
@Override
public int hashCode() {
return 31 * Arrays.hashCode(assignedSubtasks)
+ Arrays.hashCode(subtaskWeightsExcludingCloseCost);
}
@Override
public boolean equals(Object o) {
if (this == o) {
return true;
}
if (o == null || getClass() != o.getClass()) {
return false;
}
KeyAssignment that = (KeyAssignment) o;
return Arrays.equals(assignedSubtasks, that.assignedSubtasks)
&& Arrays.equals(subtaskWeightsExcludingCloseCost, that.subtaskWeightsExcludingCloseCost);
}
@Override
public String toString() {
return MoreObjects.toStringHelper(this)
.add("assignedSubtasks", assignedSubtasks)
.add("subtaskWeightsExcludingCloseCost", subtaskWeightsExcludingCloseCost)
.toString();
}
}
}